The reviewer participation becomes one of the main challenges in the tool-based code review process, since unlike formal code review, reviewers
can decide whether or not to participate in a review.
A large body of studies has found that reviewer participation is associated with software quality and code review time [1]}, [2]}, [3]}.
For instance, Kononenko et al. [4]} observed that the number of invited
reviewers have a statistically significant impact on review bugginess.
Moreover, Ruangwan et al. [5]} reported that human factors play an important role in predicting whether or not an invited reviewer will participate in a review.
To relieve the challenges of reviewer participation, many reviewer recommendation systems have been proposed.
Thongtanunam et al. [6]} introduced REVFINDER, a file location-based code-reviewer recommendation approach.
Xia et al. [7]} put textual information and file location analyses together to recommend reviewers more accurately.
Hannebauer et al. [8]} recommended code reviewers based on their expertise.
Most recently, Al-Zubaidi et al. [9]} developed a novel approach that
leverages a multi-objective meta-heuristic algorithm to search for reviewers guided by two objectives.
